DOE OSTI2016
Because of the heritage of MCNPX, the first production release of MCNP6{sup TM} used physics models, instead of ACE (A Compact Evaluated Nuclear Data File) formatted libraries, for simulating the transport of alpha particles at all energies, as MCNPX could not yet read ACE formatted light ion libraries. For non-Coulombic interactions, the physics models have suggested minimums of ∼ 20-200 MeV. The first production release of MCNP6, MCNP6{sup TM} 1.0, was still capable of transporting alphas to low energies; however, the physics models resulted in poor prediction of inelastic reactions at energies below the suggested minimums. This poorer prediction at low energies resulted in orders of magnitude lower neutron yields for events resulting from light ion interactions at low energies. For example, F-19(α,n)Na-22 from U-234 decay in UF{sub 6} produced poor neutron production results. Modeling these types of interactions correctly is vital for signal analysis for nonproliferation safeguards and nuclear security as well as for correctly characterizing source terms for radiation protection and shielding calculations. The TALYS computer code was developed to simulate nuclear reactions involving neutrons, photons, protons, deuterons, tritons, alphas and He-3 particles, in the 1 keV - 200 MeV energy range. TENDL is a nuclear data library, which provides the output of the TALYS nuclear model code system for direct use in applications (i.e. ACE formatted cross sections). Since 2008, TENDL had publicly provided ACE formatted libraries for alpha transport; however, not until the release of MCNP6{sup TM} 1.1 beta, had MCNP6 publicly possessed the capability to read such libraries. At the Radiation Protection and Shielding Division topical meeting in 2014, initial testing of MCNP6 1.1 beta showed that MCNP6 using TENDL 2012 resulted in poor prediction when compared to the compilation of experiment data provided in R. Heaton et al. Further comparisons for PuBe sources, using the MCNP6 1.1 beta spontaneous decay alpha feature coupled with alpha transport, were also given in the winter American Nuclear Society meeting in 2014, where the results showed large discrepancies with measured data. For F-19(α,n)Na-22 interactions, the R. Heaton et al. paper is a conglomeration of several important references; and the data provided by R. Heaton et al. used a numerical fit of the excitation function provided by Norman et al.. Norman et al. has recently published their (α,n) thick target yield data for PbF{sub 2} in tabular form, backing out the thick target yields of F-19(α,n)Na-22 and computing F{sub 2} and UF{sub 6} thick target neutron yields (as supplements to the PbF{sub 2} data). Here we compared measured thick target yields of E. B. Norman et al. to MCNP6 1.1 beta using TENDL 2012 libraries. Deterministically backing out the thick target yield from measured values involves using the stopping power. MCNP uses either the SPAR code or an interpolation of the Spar code and Bethe-Boch formula for computing stopping powers. As a result one may believe that these separate methods will arrive at separate thick target yields independent of the production cross section. In our MCNP calculations, we simulated the alpha until the alpha completely stopped in the medium. The energy loss in the condensed history algorithm is not continuous but discrete and related to the stopping powers. Because the neutron production yield is energy dependent, the energy loss dictates the emission of secondary neutrons; and therefore the difference in stopping powers used may have some slight effect on the yield difference. For this study, we chose to use the default condensed history algorithm setup, while future studies should address tightening steps in the condensed history algorithm to prove whether or not choice of stopping power actually matters. The fact that all 5 evaluated isotopic compositions resulted in different thick target yield percent differences does warrant this type of investigation. A typical user may not be only interested in integral emission, taking 10 s to 100 s of processor hours to simulate, but rather a timely generation of the spectra. Such an application would require biasing the production of secondaries from alpha sources such that millions of secondaries could be produced, emulating a converged energy dependent spectra. Neutron production biasing from alpha sources does not currently exist; however, developing a biasing algorithm for neutron production from alpha sources should be investigated in the future.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗